What is inverse document frequency?
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# What is inverse document frequency?

This recipe explains what is inverse document frequency

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## Recipe Objective

What is inverse document frequency? Inverse Document Frequency which measures how important a term is in a document, As we have discussed before about TF in which all terms has been considered as eaqually important. But it is known that certain terms like "is", "of" and "that", may appear lot of times in a document but have little amount of importance. So we need to weigh them down and scale up the rare ones, by using the following

IDF(A) = log_e(Total number of documents / Number of documents with term A in it)

for e.g. lets consider a document of 100 words in which the word mango appears 5 times, so the term frequency for the word mango will be 5/100 i.e 0.05, now assume that there are 10 million documents and the word mango appears in 1000 of these. Then the Inverse Document Frequency (idf) is calculated as log(10,000,000 / 1000) i.e 4.

## Step 1 - Import library and read the sample dataset

`import pandas as pd` `df = pd.read_csv("/content/drive/My Drive/Data sets/test.csv")` `df.head()`

Here we have taken a Sample dataset from kaggle of twitter Sentimental Analysis which consist of all text data.

## Step 2 - Taking only text column which is required and storing it into another DataFrame

`df2 = df.iloc[:, 1:2]` `df2.head()`

## Step 3 - Import re

`import re` `letters_only = re.sub("[^a-zA-Z]", ` ` " ", ` ` str(df2))`

Now we are importing "re" for all non-letters in the data, It will search for all non letters present into the data and replace that non-letters with spaces

## Step 4 - Import word_tokenizer and convert the text data into tokens

`from nltk.tokenize import word_tokenize` `word_tokenize(letters_only)`

## Step 5 - Split the tokenizer data and store them in a DataFrame

`letters = letters_only.split()` `df3 = pd.DataFrame(letters)` `df3.value_counts()`
```to         3
right      2
my         2
the        2
..
neverre    1
nephew     1
mindset    1
x          1
a          1
Length: 69, dtype: int64```

Here we have splitted the tokens data and converted them into DataFrame Called df3, then we will see count for each word in the df3 Data like for how many times the word has been repeated.

## Step 6 - Find out IDF

`import numpy as np` `result = np.log(len(df3) / df3.value_counts())`

Here by using the above formula for Inverse Document Frequency (IDF), we have find out the IDF for the data that we have taken and processed. For finding the IDF log is required for that we have taken numpy log.

## Step 7 - Print the result

`print("The IDF for each word in the data is:")` `print(result)`
```The IDF for each word in the data is:
to         3.205453
right      3.610918
my         3.610918
the        3.610918
...
neverre    4.304065
nephew     4.304065
mindset    4.304065
x          4.304065
a          4.304065
Length: 69, dtype: float64```

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